The correlation between sub-epidermal moisture measurement and other early indicators of pressure ulcer development-A prospective cohort observational study. Part 1. The correlation between sub-epidermal moisture measurement and ultrasound.

The correlation between sub-epidermal moisture (SEM) and other early indicators of pressure ulcer (PU) development is yet to be determined. This three-part series aims to bridge this knowledge gap, through investigating SEM and its correlation with evidence-based technologies and assessments. This article focuses on the correlation between SEM and ultrasound. A prospective cohort observational study was undertaken between February and November 2021. Patients undergoing three surgery types were consecutively enrolled to the study following informed consent. Assessments were performed prior to and following surgery for 3 days at the sacrum, both heels and a control site, using a SEM scanner and high-frequency ultrasound scanner (5-15 MHz). Spearman's rank (rs ) explored the correlation between SEM and ultrasound. A total of 60 participants were included; 50% were male with a mean age of 58 years (±13.46). A statistically significant low to moderately positive correlation was observed between SEM and ultrasound across all anatomical sites (rs range = 0.39-0.54, p < 0.05). The only exception was a correlation between SEM and ultrasound on day 0 at the right heel (rs = 0.23, p = 0.09). These results indicate that SEM and ultrasound agreed in the presence of injury; however, SEM was able to identify abnormalities before ultrasound.

Simultaneous Segmentation and Classification of Pressure Injury Image Data Using Mask-R-CNN.

Background: Pressure injuries (PIs) impose a substantial burden on patients, caregivers, and healthcare systems, affecting an estimated 3 million Americans and costing nearly $18 billion annually. Accurate pressure injury staging remains clinically challenging. Over the last decade, object detection and semantic segmentation have evolved quickly with new methods invented and new application areas emerging. Simultaneous object detection and segmentation paved the way to segment and classify anatomical structures. In this study, we utilize the Mask-R-CNN algorithm for segmentation and classification of stage 1-4 pressure injuries. Methods: Images from the eKare Inc. pressure injury wound data repository were segmented and classified manually by two study authors with medical training. The Mask-R-CNN model was implemented using the Keras deep learning and TensorFlow libraries with Python. We split 969 pressure injury images into training (87.5%) and validation (12.5%) subsets for Mask-R-CNN training. Results: We included 121 random pressure injury images in our test set. The Mask-R-CNN model showed overall classification accuracy of 92.6%, and the segmentation demonstrated 93.0% accuracy. Our F1 scores for stages 1-4 were 0.842, 0.947, 0.907, and 0.944, respectively. Our Dice coefficients for stages 1-4 were 0.92, 0.85, 0.93, and 0.91, respectively. Conclusions: Our Mask-R-CNN model provides levels of accuracy considerably greater than the average healthcare professional who works with pressure injury patients. This tool can be easily incorporated into the clinician's workflow to aid in the hospital setting.

In vivo quantification of 3D displacement in sacral soft tissues under compression: Relevance of 2D US-based measurements for pressure ulcer risk assessment.

OBJECTIVE: 2D Ultrasound (US) imaging has been recently investigated as a more accessible alternative to 3D Magnetic Resonance Imaging (MRI) for the estimation of soft issue motion under external mechanical loading. In the context of pressure ulcer prevention, the aim of this pilot MRI study was to design an experiment to characterize the sacral soft tissue motion under a controlled mechanical loading. Such an experiment targeted the estimation of the discrepancy between tissue motion assessed using a 2D imaging modality (echography) versus tissue motion assessed using a (reference) 3D imaging modality (MRI). METHODS: One healthy male volunteer participated in the study. An MRI-compatible custom-made setup was designed and used to load the top region of the sacrum with a 3D-printed copy of the US transducer. Five MR images were collected, one in the unloaded and four in the different loaded configurations (400-1200 [g]). Then, a 3D displacement field for each loading configuration was extracted based on the results of digital volume correlation. Tissue motion was separated into the X, Y, Z directions of the MRI coordinate system and the ratios between the out-of-plane and in-plane components were assessed for each voxel of the selected region of interest. RESULTS: Ratios between the out-of-plane and in-plane displacement components were higher than 0.6 for more than half of the voxels in the region of interest for all load cases and higher than 1 for at least quarter of the voxels when loads of 400-800 [g] were used. CONCLUSION: The out-of-ultrasound-plane tissue displacement was not negligible, therefore 2D US imaging should be used with caution for the evaluation of the tissue motion in the sacrum region. The 3D US modality should be further investigated for this application.

MR-based quantitative measurement of human soft tissue internal strains for pressure ulcer prevention.

Pressure ulcers are a severe disease affecting patients that are bedridden or in a wheelchair bound for long periods of time. These wounds can develop in the deep layers of the skin of specific parts of the body, mostly on heels or sacrum, making them hard to detect in their early stages. Strain levels have been identified as a direct danger indicator for triggering pressure ulcers. Prevention could be possible with the implementation of subject-specific Finite Element (FE) models. However, generation and validation of such FE models is a complex task, and the current implemented techniques offer only a partial solution of the entire problem considering only external displacements and pressures, or cadaveric samples. In this paper, we propose an in vivo solution based on the 3D non-rigid registration between two Magnetic Resonance (MR) images, one in an unloaded configuration and the other deformed by means of a plate or an indenter. From the results of the image registration, the displacement field and subsequent strain maps for the soft tissues were computed. An extensive study, considering different cases (on heel pad and sacrum regions) was performed to evaluate the reproducibility and accuracy of the results obtained with this methodology. The implemented technique can give insight for several applications. It adds a useful tool for better understanding the propagation of deformations in the heel soft tissues that could generate pressure ulcers. This methodology can be used to obtain data on the material properties of the soft tissues to define constitutive laws for FE simulations and finally it offers a promising technique for validating FE models.

Wound tissue segmentation by computerised image analysis of clinical pressure injury photographs: a pilot study.

OBJECTIVE: Wound tissues can provide ample information about the wound development and healing process. However, the manual identification and measurement of wound tissue types is time-consuming and challenging due to the complexities of pressure injuries (PI). This study aims to develop an image analysis algorithm to automatically identify and differentiate wound tissue types from PI wound beds. METHOD: This was a cross-sectional algorithm development study. PI photographs were obtained from a western Pennsylvania hospital. We used our previously developed wound bed segmentation tool to identify PI wound beds. We then used the k-means clustering method to classify the subzones on the wound beds. Finally, the support vector machine classifier was used to identify the classified subzones to certain types of wound tissue. RESULTS: An image analysis algorithm was developed, using 64 selected PI photographs, to automatically identify different wound tissues for PIs. CONCLUSION: Validation of the wound tissue identification of the PIs by image analysis algorithm demonstrated that our image analysis algorithm is a reliable and objective approach to monitoring wound healing progress through clinical PI photographs, and offers new insight into PI evaluation and documentation. DECLARATION OF INTEREST: The authors have no conflicts of interest to declare.

Effect Evaluation of Platelet-Rich Plasma Combined with Vacuum Sealing Drainage on Serum Inflammatory Factors in Patients with Pressure Ulcer by Intelligent Algorithm-Based CT Image.

This work was to explore the efficacy of intelligent algorithm-based computed tomography (CT) to evaluate platelet-rich plasma (PRP) combined with vacuum sealing drainage (VSD) in the treatment of patients with pressure ulcers. Based on the u-net network structure, an image denoising algorithm based on double residual convolution neural network (Dr-CNN) was proposed to denoise the CT images. A total of 84 patients who were hospitalized in hospital were randomly divided into group A (without any intervention), group B (PRP treatment), group C (VSD treatment), and group D (PRP+VSD treatment). Procalcitonin (PCT) was detected by enzyme-linked immunosorbent assay (ELISA) combined with immunofluorescence method, C-reactive protein (CRP) was detected by rate reflectance turbidimetry (RRT), and interleukin-6 (IL-6) was detected by electrochemiluminescence method. The results showed that after treatment, 44 cases (52.38%) of pressure ulcers patients recovered, 24 cases (28.57%) had no change in stage, and 16 cases (19.04%) developed pressure ulcers. The pain scores of group D at 1 week (3.35 ± 0.56 points) and 2 weeks (2.76 ± 0.55 points) after treatment were significantly lower than those in group C (7.77 ± 0.58 points and 6.34 ± 0.44 points, respectively). The time of complete wound healing in group D (24.5 ± 2.32) was obviously lower in contrast to that in groups A, B, and C (35.54 ± 3.22 days, 30.23 ± 2 days, and 29.34 ± 2.15 days, respectively). In addition, the medical satisfaction of group D (8.74 ± 0.69) was significantly higher than that of groups A, B, and C (4.69 ± 0.85, 5.22 ± 0.31, and 5.18 ± 0.59, respectively). The levels of IL-6 and PCT in group D were lower than those in groups A, B, and C, and the differences were statistically significant (P < 0.01). The average values of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) of the Dr-CNN network model were 37.21 ± 1.09 dB and 0.925 ± 0.01, respectively, which were higher than other algorithms. The mean values of root mean square error (MSE) and normalized mean absolute distance (NMAD) of the Dr-CNN network model were 0.022 ± 0.002 and 0.126 ± 0.012, respectively, which were significantly lower than other algorithms (P < 0.05). The experimental results showed that PrP combined with VSD could significantly reduce the inflammatory response of patients with pressure ulcers. PRP combined with VSD could significantly reduce the pain of dressing change for patients. Moreover, the performance model of image denoising algorithm based on double residual convolutional neural network was better than other algorithms.

Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis.

A pressure ulcer is an injury of the skin and underlying tissues adjacent to a bony eminence. Patients who suffer from this disease may have difficulty accessing medical care. Recently, the COVID-19 pandemic has exacerbated this situation. Automatic diagnosis based on machine learning (ML) brings promising solutions. Traditional ML requires complicated preprocessing steps for feature extraction. Its clinical applications are thus limited to particular datasets. Deep learning (DL), which extracts features from convolution layers, can embrace larger datasets that might be deliberately excluded in traditional algorithms. However, DL requires large sets of domain specific labeled data for training. Labeling various tissues of pressure ulcers is a challenge even for experienced plastic surgeons. We propose a superpixel-assisted, region-based method of labeling images for tissue classification. The boundary-based method is applied to create a dataset for wound and re-epithelialization (re-ep) segmentation. Five popular DL models (U-Net, DeeplabV3, PsPNet, FPN, and Mask R-CNN) with encoder (ResNet-101) were trained on the two datasets. A total of 2836 images of pressure ulcers were labeled for tissue classification, while 2893 images were labeled for wound and re-ep segmentation. All five models had satisfactory results. DeeplabV3 had the best performance on both tasks with a precision of 0.9915, recall of 0.9915 and accuracy of 0.9957 on the tissue classification; and a precision of 0.9888, recall of 0.9887 and accuracy of 0.9925 on the wound and re-ep segmentation task. Combining segmentation results with clinical data, our algorithm can detect the signs of wound healing, monitor the progress of healing, estimate the wound size, and suggest the need for surgical debridement.

MR-compatible loading device for assessment of heel pad internal tissue displacements under shearing load.

In the last decade, the role of shearing loads has been increasingly suspected to play a determinant impact in the formation of deep pressure ulcers. In vivo observations of such deformations are complex to obtain. Previous studies only provide global measurements of such deformations without getting the quantitative values of the loads that generate these deformations. To study the role that shearing loads have in the etiology of heel pressure ulcers, an MR-compatible device for the application of shearing and normal loads was designed. Magnetic resonance imaging is a key feature that allows to monitor deformations of soft tissues after loading in a non-invasive way. Measuring applied forces in an MR-environment is challenging due to the impossibility to use magnetic materials. In our device, forces are applied through the compression of springs made of polylactide. Shearing and normal loads were applied on the plantar skin of the human heel through a flat plate while acquiring MR images. The device materials did not introduce any imaging artifact and allowed for high quality MR deformation measurements of the internal components of the heel. The obtained subject-specific results are an original data set that can be used in validations for Finite Element analysis and therefore contribute to a better understanding of the factors involved in pressure ulcer development.

Volumetric Evaluation of Dead Space in Ischial Pressure Injuries Using Magnetic Resonance Imaging: A Case Series.

OBJECTIVE: To establish a preoperative evaluation procedure by measuring the volume of dead space using MRI in patients with ischial pressure injuries. METHODS: Patients with spinal cord injury and ischial pressure injuries who underwent treatment between August 2016 and November 2019 were included in the study. Preoperative MRI scan was conducted on all patients. The volume estimation and three-dimensional (3D) reconstruction were performed based on MRI data using a 3D Slicer. Based on the resulting volume, a muscle flap that could fit the dead space was selected. Surgery was performed with the selected muscle flap, and a fasciocutaneous flap was added, if necessary. RESULTS: A total of eight patients with ischial pressure injuries were included in the study. The mean patient age was 59.0 ± 11.0 years. The mean body mass index was 26.62 ± 3.89 kg/m2. The mean volume of dead space was 104.75 ± 81.05 cm3. The gracilis muscle was the most selected muscle flap and was used in four patients. In five of eight cases, a fasciocutaneous flap was used as well. The mean follow-up period was 16 months, and by that point, none of the patients evinced complications that required surgery. CONCLUSIONS: To the authors' knowledge, this is the first report on volumetric evaluation of dead space in ischial pressure injuries. The authors believe that the 3D reconstruction process would enable adequate dead space obliteration in ischial pressure injuries. The authors propose that preoperative MRI scans in patients with ischial pressure injury should become an essential part of the process.

Extraabdominal Rupture of an Inflammatory Abdominal Aortic Aneurysm in a Patient With Aortodecubital Fistula: A Case Report.

Aortodecubital fistula is a pathologic communication between aorta and a decubitus ulcer. It is very rarely encountered vascular condition in abdominal aortic aneurysms (AAA), with difficult diagnostics and high mortality. Patients often present with systemic and local infection and are at risk for hemorrhage. We present a paraplegic patient with fistulous communication between an inflamed abdominal aortic aneurysm and a sacral decubitus ulcer, leading to intermittent bleeding episodes and finally to exsanguination. While extremely rare, this case emphasizes the need for early, accurate diagnosis and salvage intervention when possible.

Positron emission tomography study of effects of two pressure-relieving support surfaces on pressure ulcer development.

OBJECTIVE: To study the pathophysiological cascade of pressure ulcer (PU) development consisting of tissue deformation, inflammation and hypoxia. METHOD: In this crossover study, deformation was measured with computerised tomography (CT) linked with contact area reflecting immersion and envelopment. Inflammation and hypoxia were measured using subepidermal moisture (SEM), skin temperature and tissue perfusion with positron emission tomography. These variables were investigated under 90 minutes of pressure exposure caused by two functionally different support surfaces-a regular foam mattress and a minimum pressure air (MPA) mattress. RESULTS: A total of eight healthy volunteers took part in the study. There was major tissue deformation when the participants lay on a foam mattress while the tissues retained their original shape on the MPA mattress (p<0.0001). During the pressure exposure, the skin temperature increased significantly on both support surfaces but the final temperature on the foam mattress was about 1oC higher than on the MPA mattress (p<0.0001). SEM increased on both support surfaces compared with an unexposed reference site, but the cause may be different between the two support surfaces. Tissue perfusion was lowest in the skin followed by subcutaneous tissues and highest in the muscles. The pressure exposure did not cause any substantial changes in perfusion. The results showed that tissue deformation was more pronounced, the support surface contact area (envelopment), was smaller and the skin temperature higher on the foam mattress than on the MPA mattress, without significant differences in tissue perfusion. CONCLUSION: In this study, the MPA mattress support surface had mechanobiological properties that counteracted tissue deformation and thereby may prevent PUs.

Relationship between ultrasonographically low-echoic lesions under the skin, wheelchair sitting time, and interface pressure on ischial region in individuals with chronic spinal cord injury.

Objective: To determine the relationship between physical findings, wheelchair sitting time, and interface pressure on ischial region in subjects with spinal cord injury (SCI).Design: Cross-sectional study.Setting: Rehabilitation center in Japan.Participants: Manual wheelchair users with chronic SCI (n = 45).Interventions: Pressure ulcers (PU) were diagnosed by inspection, palpation, and ultrasonography. Self-reports were obtained on wheelchair sitting time and pressure mapping was recorded while the subject was seated on the wheelchair.Outcome measures: Subjects were divided into those with ultrasonographically low-echoic lesions (PU-positive group, n = 11) and no such lesions (PU-negative group, n = 34). Outcome measures included wheelchair sitting time and interface pressure at bilateral ischial regions.Results: Using ultrasonography, 13 low-echoic lesions were identified in 11 subjects of the PU-positive group. The pressure duration was longer and interface pressure was significantly higher in subjects of the PU-positive group compared with those of the PU-negative group (P < 0.05 and P < 0.001, respectively).Conclusions: This is the first study to evaluate the interrelationship between physical findings, sitting time, and ultrasonographically measured interface pressure on ischial region area in subjects with spinal cord injury. To prevent pressure ulcers, we recommend avoidance of prolonged wheelchair sitting and measures that can reduce the interface pressure. These variables should be carefully tailored to the needs of the individual subjects with SCI.

Diagnostic Accuracy of Point-of-Care Fluorescence Imaging for the Detection of Bacterial Burden in Wounds: Results from the 350-Patient Fluorescence Imaging Assessment and Guidance Trial.

Objective: High bacterial load contributes to chronicity of wounds and is diagnosed based on assessment of clinical signs and symptoms (CSS) of infection, but these characteristics are poor predictors of bacterial burden. Point-of-care fluorescence imaging (FL) MolecuLight i:X can improve identification of wounds with high bacterial burden (>104 colony-forming unit [CFU]/g). FL detects bacteria, whether planktonic or in biofilm, but does not distinguish between the two. In this study, diagnostic accuracy of FL was compared to CSS during routine wound assessment. Postassessment, clinicians were surveyed to assess impact of FL on treatment plan. Approach: A prospective multicenter controlled study was conducted by 20 study clinicians from 14 outpatient advanced wound care centers across the United States. Wounds underwent assessment for CSS followed by FL. Biopsies were collected to confirm total bacterial load. Three hundred fifty patients completed the study (138 diabetic foot ulcers, 106 venous leg ulcers, 60 surgical sites, 22 pressure ulcers, and 24 others). Results: Around 287/350 wounds (82%) had bacterial loads >104 CFU/g, and CSS missed detection of 85% of these wounds. FL significantly increased detection of bacteria (>104 CFU/g) by fourfold, and this was consistent across wound types (p < 0.001). Specificity of CSS+FL remained comparably high to CSS (p = 1.0). FL information modified treatment plans (69% of wounds), influenced wound bed preparation (85%), and improved overall patient care (90%) as reported by study clinicians. Innovation: This novel noncontact, handheld FL device provides immediate, objective information on presence, location, and load of bacteria at point of care. Conclusion: Use of FL facilitates adherence to clinical guidelines recommending prompt detection and removal of bacterial burden to reduce wound infection and facilitate healing.

Ultrasound assessment of deep tissue on the wound bed and periwound skin: A classification system using ultrasound images.

AIMS: Given the utility of ultrasonography in assessing pressure injury, some ultrasonographic findings have already been used as indicators of deep tissue pressure injury. Despite reports showing that a cloud-like ultrasonographic pattern reflected the presence of deep tissue necrosis, identifying cloud-like patterns was difficult given the presence of similar findings, such as a cobblestone-like pattern. This case series reports patients with pressure injuries who presented with a cloud-like (five cases) and cobblestone-like (four cases) pattern during ultrasonography. METHODS: This study was conducted at a Japanese university hospital. Participants included patients who underwent routine examination by an interdisciplinary pressure injury team. Pressure injury severity was assessed using the DESIGN-R® scoring system and the wound size were measured using ImageJ software based on the wound photograph. RESULTS: Among the five cases showing a cloud-like pattern upon ultrasonography, all exhibited an increase in the total DESIGN-R® score, while three exhibited an increase in wound size. On the other hand, all four cases showing a cobblestone-like pattern displayed no increase in the total DESIGN-R® score and a decrease in wound size. CONCLUSION: This study suggested that distinguishing between cloud-like and cobblestone-like ultrasonography patterns is necessary for determining the presence or absence of deep tissue pressure injury. In order to comprehensively assess pressure injuries with ultrasonography, future studies should be conducted in a large number of participants.

Automatic measurement of pressure ulcers using Support Vector Machines and GrabCut.

BACKGROUND AND OBJECTIVE: Pressure ulcers are regions of trauma caused by a continuous pressure applied to soft tissues between a bony prominence and a hard surface. The manual monitoring of their healing evolution can be achieved by area assessment techniques that include the use of rulers and adhesive labels in direct contact with the injury, being highly inaccurate and subjective. In this paper we present a Support Vector Machine classifier in combination with a modified version of the GrabCut method for the automatic measurement of the area affected by pressure ulcers in digital images. METHODS: Three methods of region segmentation using the superpixel strategy were evaluated from which color and texture descriptors were extracted. After the superpixel classification, the GrabCut segmentation method was applied in order to delineate the region affected by the ulcer from the rest of the image. RESULTS: Experiments on a set of 105 pressure ulcer images from a public data set resulted in an average accuracy of 96%, sensitivity of 94%, specificity of 97% and precision of 94%. CONCLUSIONS: The association of support vector machines with superpixel segmentation outperformed current methods based on deep learning and may be extended to tissue classification.

Ultrasound elastography reveals the relation between body posture and soft-tissue stiffness which is relevant to the etiology of sitting-acquired pressure ulcers.

OBJECTIVE: Sitting-acquired pressure ulcers (PUs) are common in wheelchair users. These PUs are often serious and may involve deep tissue injury (DTI). Investigating the mechanical properties of the tissues susceptible to DTI may help in guiding the prevention and early detection of PUs. In this study, shear wave elastography (SWE) was used to measure the normative mechanical properties of the soft tissues of the buttocks, i.e. skeletal muscle and subcutaneous fat, under the ischial tuberosities, in a convenient sample of healthy adults without weight bearing and with weight bearing of different times. APPROACH: We compared the stiffness properties of these soft tissues between the lying prone and sitting postures, to determine whether there are detectable property changes that may be associated with the type of posture. We hypothesized that muscle contractions and 3D tissue configurations associated with the posture may influence the measured tissue stiffnesses. MAIN RESULTS: Our results have shown that indeed, SWE values differed significantly across postures, but not over time in a specific posture or for the right versus left sides of the body. SIGNIFICANCE: We have therefore demonstrated that soft-tissue stiffness increases when sitting with weight bearing and may contribute to increasing the potential PU risk in sitting compared to lying prone, given the stiffer behavior of tissues observed in sitting postures.

An evaluation of dermal microcirculatory occlusion under repeated mechanical loads: Implication of lymphatic impairment in pressure ulcers.

OBJECTIVE: Pressure ulcers are caused by prolonged mechanical loads deforming the underlying soft tissues. However, the mechanical loads for microcirculatory occlusion are unknown. The present study was designed to characterize the simultaneous response of microvascular and lymphatic structures under repeated mechanical loading. METHODS: The effects of two distinct loading/unloading cycles involving (a) incremental pressures 30, 60, and 90 mmHg and (b) three repeated cycles of 30 mmHg were evaluated on a cohort of able-bodied volunteers. Microvascular response involved the monitoring of transcutaneous gas tensions, while dermal lymphatic activity was estimated from near-infrared imaging. Responses were compared during each load and recovery cycle. RESULTS: Changes in microvascular response were dependent on the load magnitudes, with 30 mmHg resulting in a reduction in oxygen tension only, while 90 mmHg affected both oxygen and carbon dioxide values in most cases (54%). By contrast, lymphatics revealed near total occlusion at 30 mmHg. Although there were intersubject differences, temporal trends consistently revealed partial or full impairment under load, with recovery during off-loading. CONCLUSIONS: The pressure required to cause microcirculatory occlusion differed between individuals, with lymphatic impairment occurring at a lower pressure to that of microvascular vessels. This highlights the need for personalized care strategies and regular off-loading of vulnerable tissues.

Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning.

Pressure injuries represent a major concern in many nations. These wounds result from prolonged pressure on the skin, which mainly occur among elderly and disabled patients. If retrieving quantitative information using invasive methods is the most used method, it causes significant pain and discomfort to the patients and may also increase the risk of infections. Hence, developing non-intrusive methods for the assessment of pressure injuries would represent a highly useful tool for caregivers and a relief for patients. Traditional methods rely on findings retrieved solely from 2D images. Thus, bypassing the 3D information deriving from the deep and irregular shape of this type of wounds leads to biased measurements. In this paper, we propose an end-to-end system which uses a single 2D image and a 3D mesh of the pressure injury, acquired using the Structure Sensor, and outputs all the necessary findings such as: external segmentation of the wound as well as its real-world measurements (depth, area, volume, major axis and minor axis). More specifically, a first block composed of a Mask RCNN model uses the 2D image to output the segmentation of the external boundaries of the wound. Then, a second block matches the 2D and 3D views to segment the wound in the 3D mesh using the segmentation output and generates the aforementioned real-world measurements. Experimental results showed that the proposed framework can not only output refined segmentation with 87% precision, but also retrieves reliable measurements, which can be used for medical assessment and healing evaluation of pressure injuries.